The present invention relates to an ultrasonographic device which obtains images by transmitting and receiving ultrasounds to and from a subject, and more particularly to an ultrasonographic device provided with a function to subject the obtained image to image improvement processing by image processing and a method for improving the image quality of the ultrasonographic device.
Ultrasonographic devices are used in examining various regions within the body including the abdomen and the heart. Ultrasonographic devices, having many advantages including the absence of harm to living bodies unlike X-ray examining devices, handling ease and capability of permitting moving image observation on a real time basis, are extensively utilized. An ultrasonographic device irradiates ultrasounds from an ultrasound probe toward a subject, receives reflected waves from tissues within the subject with the ultrasound probe, and displays the received information on a monitor. By scanning multiple parts with ultrasounds focused in a specific direction, a two-dimensional image can be obtained on a real time basis. The types of ultrasound images include B mode images obtained by converting the reflectance of a tissue of a living subject into the brightness of pixel values, Doppler images having information on the moving velocity of the living body tissue, tissue elastographs having hue information according to the distortion quantity or elasticity of the living body tissue, and synthetic images obtained by synthesizing information contained in these images.
However, a two-dimensional image obtained by the ultrasonographic device contains speckle noise generated by the interference of multiple reflected waves from fine structures within the living body. Further more, as received signals obtained with the ultrasound probe are bandwidth-limited signals, high frequency components which should otherwise be obtained on the tissue boundary may fail to be sufficiently obtained, and invite blurring of edges contained in the image. These phenomena including the occurrence of speckle noise and the blurring of edges give rise to quality deterioration of the image, which would adversely affect diagnosis. In order to accurately read and image important structures of morbid regions among others, it is required to display ultrasound images reduced in speckle noise and so processed as to sharpen edges.
Techniques for reducing speckle noise include frequency compounding and spatial compounding. Frequency compounding is a technique by which multiple images are generated by irradiating ultrasounds differing in frequency toward the same region and a single image by summing and averaging those images.
As the pattern of speckle noise substantially varies with the frequency of the ultrasounds used and, on the other hand the reflected waves from the tissue boundary or elsewhere are less subject to variations with the frequency, speckle noise can be reduced by the summing and averaging. However, since the frequency is used in a divided way in frequency compounding, there is a problem that the frequency band of the image is narrowed and edges are blurred. On the other hand, spatial compounding is a technique by which multiple images are generated by irradiating the same region with ultrasounds in different directions and a single image is obtained by summing and averaging those images. According to this technique, speckle noise is reduced by utilizing the variation of the pattern of speckle noise with the irradiating direction of ultrasounds. However, as obtaining a single image takes a long time by spatial compounding, there is a problem of a drop in image displaying velocity.
On the other hand, as a method different from those referred to above, there is a noise reducing technique using image processing. The advancement of performance enhancement and cost reduction of image processors in recent years has made it relatively ease to mount on hardware complex image processing, whose practical application previously was difficult in respect of processing speed. Long established noise reducing techniques, such as the one using a smoothing filter, are known to involve problems of edge blurring and loss of vital signals, and noise removing techniques using multiple resolution analysis, typically wavelet transform and Laplacian pyramid transform, and edge sharpening techniques (for instance Patent Documents 1 through 3).
More recently, as more sophisticated multiple resolution analyzing systems, Curvelet transform (Non-Patent Document 1), Contourlet transform (Non-Patent Document 2), complex Wavelet transform (Non-Patent Document 3) and steerable pyramid transform (Non-Patent Document 4) have been proposed.
Furthermore, the application of these sophisticated multi-level resolving systems to ultrasonographic devices is also proposed in Non-Patent Document 5.
In the conventional Wavelet transform, the edge direction is divided into three, and in the Laplacian pyramid transform, the edge direction is only one, the sophisticated multi-level resolving systems allow the edge direction to be divided into four or more. In the context, the edge direction is divided into K means resolution at each resolution level and in each position into K resolution coefficients vividly reacting to a pattern having brightness variations in K types of mutually different directions. Whereas an image is expressed in resolution coefficients in three edge directions including vertical (0°), transverse (90°) and oblique (45° and 135°) in the conventional Wavelet transform, in this transform an edge in the 45° direction and another in the 135° direction cannot be distinguished from each other. In order to accomplish higher performance in image quality improvement, it is essential to use a multi-level resolving system whose edge direction is divided into at least four.
According to the image quality improving technique based on a multi-level resolution, the intensities of resolution coefficients are converted on the basis of the estimated amount of noise usually contained in each resolution coefficient. Thus, by reconstructing the image, after conserving or emphasizing the intensities of resolution coefficients estimated to contain large amounts of signal components and, conversely, reducing the intensities of resolution coefficients estimated to contain large amounts of noise components, from the resolution coefficients, an image reduced in noise and having sharpened edges can be obtained. Therefore, the estimation if the noise amount and the intensity conversion of the resolution coefficients are processing steps of vital importance.
Patent Document 1: U.S. Pat. No. 5,497,777
Patent Document 2: Japanese Patent Application Laid-Open Publication No. 2006-116307
Patent Document 3: Japanese Patent Application Laid-Open Publication No. 2005-296331
Non-Patent Document 1: J. L. Starck, E. J. Candes, et al.: IEEE Trans. Image Processing 11, 6, pp. 670-684 (2002)
Non-Patent Document 2: M. N. Do, M. Vetterli: IEEE Trans. Image Processing, 14, 12, PP. 2091-2106 (2005)
Non-Patent Document 3: N. G. Kingsbury: Proceedings of European Signal Processing Conference, pp. 319-322 (1998)
Non-Patent Document 4: E. P. Simoncelli, W. T. Freeman: Proceedings of IEEE International Conference on Image Processing, 3, PP. 444-447 (1995)
Non-Patent Document 5: E. H. O. Ng: Applied science in electrical and computer engineering, University of Waterloo (Master thesis), pp. 1-112 (2005)